Segmentation of Diabetic Retinopathy Based on Retinal Fundus Images Using Thresholding Technique

Abstract

Diabetic Retinopathy is damage to the retina caused by complications of diabetes mellitus. Exudates are the primary sign of Diabetic Retinopathy identified on the opthalmoscope as white or yellowish areas with varying sizes, shapes and locations in the retina. Early detection can potentially reduce the risk of blindness. Diabetic retinopathy diagnosis for early-stage detection used a manual or semi-automated system that only an ophthalmologist can do it. This project proposed an automatic analysis of diabetic retinopathy and lesion of the eye in the retinal region using image processing techniques. The proposed technique is using thresholding technique. The data is taken from the DIRECTDB1 database. Preprocessing is applied first and then the image is segmented into 8 x 8 regions. An image histogram is then been calculated at each region to look for the maximum number of pixels for each intensity level. By comparing normal and exudate regions, the best threshold is found. The proposed technique is validated based on the accuracy, specificity, and sensitivity of exudate detection from the ground truth image reference. The mean from the range of the abnormal region of interest (ROI) is also calculated whether it is within range of the optimal value. In conclusion, adaptive thresholding is the method of choice to develop an automated detection of diabetic retinopathy. The result shows an accuracy of 65.79%, sensitivity is 31.58%, and specificity is 100%. The best segmentation and classification performance is achieved in the range of abnormal region ROI which is 0.35 to 0.55.

Brain cone beam computed tomography image analysis using ResNet50 for collateral circulation classification

“…A similar method can be implemented for head CBCT scan images. This involves image segmentation, and the processed images will be trained by using the deep learning approach to detect hemorrhage in the scan [32]. The result can be classified into cases with hemorrhage and without hemorrhage at high accuracy [28], [31].…”